Description This data release contains the light curve models, raw forced photometry, and derived parameters for Type II SNe analysed in the paper "Inferring CSM Properties of Type II SNe Using a Magnitude-Limited ZTF Sample". The dataset includes photometric observations from the Zwicky Transient Facility (ZTF) Bright Transient Survey (BTS) and the resulting model fits with multi-output Gaussian Process regression used to infer circumstellar material (CSM) properties around Type II SN progenitors. Contents 1. ZTF Forced Photometry Raw forced photometry files for 493 Type II (incl. Type II, Type IIb and Type IIn) SNe from the ZTF Bright Transient Survey Data format: Within Hinds_25_lcs, CSV files containing time series photometry in g, r, and i bands Includes observation dates (MJD), flux, flux uncertainties, filter_ids and quality flags Filenames follow the convention: [ZTFID]_BTS_fnu.csv 2. Light Curve Models Best-fit light curve models in mag space for each supernova using CSM interaction models Models generated using the methodology described in the associated paper Data format: Within Hinds_25_lcs, CSV files containing light curve predictions Filenames follow the convention: [ZTFID]_LC.csv 3. Parameter Table Comprehensive table of derived parameters for all SNe in the sample Includes physical parameters: rise time, absolute magnitude, CSM mass and CSM radius, among others Data format: machine-readable table (produced with astropy) Filename: Hinds_ea_25.dat Methods The photometric data were obtained through the ZTF forced photometry service, see A. A. Miller et al., (in prep.). Light curve modelling using multi-output Gaussian Processes. Full details of the methodology can be found in the associated paper. Citation If you use this data in your research, please cite: Hinds et al. (2025). "Inferring CSM Properties of Type II SNe Using a Magnitude-Limited ZTF Sample."

Execution time (seconds) for selected values in k with different number of features in data via k-mer analysis.

This link consists of 10 anonymized non-small cell lung cancer (NSCLC) field of Views (FoVs) to test Mistic.

Mistic

Understanding the complex ecology of a tumor tissue and the spatio-temporal relationships between its cellular and microenvironment components is becoming a key component of translational research, especially in immune-oncology. The generation and analysis of multiplexed images from patient samples is of paramount importance to facilitate this understanding. In this work, we present Mistic, an open-source multiplexed image t-SNE viewer that enables the simultaneous viewing of multiple 2D images rendered using multiple layout options to provide an overall visual preview of the entire dataset. In particular, the positions of the images can be taken from t-SNE or UMAP coordinates. This grouped view of all the images further aids an exploratory understanding of the specific expression pattern of a given biomarker or collection of biomarkers across all images, helps to identify images expressing a particular phenotype or to select images for subsequent downstream analysis. Currently there is no freely available tool to generate such image t-SNEs.

Links

Mistic code

Mistic documentation

Paper

47,294 highly cited papers in the Essential Science Indicators (ESI) were used to test the proposed visualization approach for science mapping.

In our research paper, the highly cited papers were visualized using three network embedding models plus the t-SNE dimensionality reduction technique. Besides, three base maps were created with the same dataset for evaluation purposes. All base maps used the classic OpenOrd method with different edge cutting strategies and parameters.

We are publishing the dataset to allow other researchers to create a roughly equivalent experiment based on the highly-cited papers.

The download date is 2018 March. File "RF201803_TopPaper_FULL_links.gexf" is the Gephi network file created with 47,294 highly cited paper and all 3.6 million co-citation relationships. File "RF201803_TopPaper_top15_Edges.net" is the network file with the top 15 highest weight edges per node relationships. File "RF201803_TopPaper_UT_FIELD.csv" contains all paper's UT, Field, Publish Year, Cites data.

The data downloaded from the Essential Science Indicator web site: https://esi.clarivate.com/

The timely and accurate diagnosis of severe faults in the high-speed train air compressor is crucial due to the potential for significant safety issues. In response to this problem

UMAP

Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualization similarly to t-SNE, but also for general non-linear dimension reduction. The algorithm is founded on three assumptions about the data:

The data is uniformly distributed on a Riemannian manifold; The Riemannian metric is locally constant (or can be approximated as such); The manifold is locally connected. From these assumptions, it is possible to model the manifold with a fuzzy topological structure. The embedding is found by searching for a low dimensional projection of the data that has the closest possible equivalent fuzzy topological structure.

The details for the underlying mathematics can be found in our paper on ArXiv:

McInnes, L, Healy, J, UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, ArXiv e-prints 1802.03426, 2018